Detecting cognitive distraction using random forest by considering eye movement type

Hiroaki Koma, Taku Harada, Akira Yoshizawa, Hirotoshi Iwasaki

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Detecting distracted states can be applied to various problems such as danger prevention when driving a car. A cognitive distracted state is one example of a distracted state. It is known that eye movements express cognitive distraction. Eye movements can be classified into several types. In this paper, the authors detect a cognitive distraction using classified eye movement types when applying the Random Forest machine learning algorithm, which uses decision trees. They show the effectiveness of considering eye movement types for detecting cognitive distraction when applying Random Forest. The authors use visual experiments with still images for the detection.

Original languageEnglish
Title of host publicationIntelligent Systems
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages1587-1599
Number of pages13
ISBN (Electronic)9781522556442
ISBN (Print)1522556435, 9781522556435
DOIs
Publication statusPublished - 4 Jun 2018

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